Figure - available from: Scientific Reports
This content is subject to copyright. Terms and conditions apply.
Examination of structural features depicting adenosine-dependent receptor activation in A2aR from 2 µs -length MD simulations. Lipid (DOPC, DOPG) and ligand (adenosine, APO) experimental conditions were considered. 4 replicas for each condition combination were run. Structures were taken every 0.02 µs. (A) The TM3–TM6 distance is measured between Cα atoms of R1023.50 and E2886.30. (B) The TM3–TM7 distance is measured between Cα atoms of R1023.50 and Y2887.53. Horizontal TM3–TM6 and TM3–TM7 red lines correspond to the distances between the aforementioned respective atoms for the inactive receptor (PDB entry: 4EIY)¹⁶ whereas horizontal green lines correspond to the distances for the active receptor (PDB entry: 6GDG)¹⁷. ADN stands for adenosine. Figures adapted from⁷.
Source publication
Molecular dynamics (MD) is the common computational technique for assessing efficacy of GPCR-bound ligands. Agonist efficacy measures the capability of the ligand-bound receptor of reaching the active state in comparison with the free receptor. In this respect, agonists, neutral antagonists and inverse agonists can be considered. A collection of MD...
Citations
... Notably, TM1 undergoes an inward motion, whereas TM2 moves outward on the extracellular side [15]. These shifts are complemented by subtle inward movements of TM3, TM4, and TM7 on the intracellular side, facilitating the receptor's activation [15,[37][38][39]. Additionally, TM6 displays an outward movement, a characteristic shared by many members of the GPCR superfamily [15,[37][38][39][40]. ...
... These shifts are complemented by subtle inward movements of TM3, TM4, and TM7 on the intracellular side, facilitating the receptor's activation [15,[37][38][39]. Additionally, TM6 displays an outward movement, a characteristic shared by many members of the GPCR superfamily [15,[37][38][39][40]. ...
... As described by [37], a recognized behavior in GPCR activation involves specific movements of TM helices: typically, TM6 extends outward relative to TM3, and TM7 retracts inward relative to TM3. Our simulations, initiated from the active crystal structure, displayed the reverse of these typical movements. ...
The MC1R protein is a receptor found in melanocytes that plays a role in melanin synthesis. Mutations in this protein can impact hair color, skin tone, tanning ability, and increase the risk of skin cancer. The MC1R protein is activated by the alpha‐melanocyte‐stimulating hormone (α‐MSH). Previous studies have shown that mutations affect the interaction between MC1R and α‐MSH; however, the mechanism behind this process is poorly understood. Our study aims to shed light on this mechanism using molecular dynamics (MD) simulations to analyze the Asp84Glu and Asp294His variants. We simulated both the wild‐type (WT) protein and the mutants with and without ligand. Our results reveal that mutations induce unique conformations during state transitions, hindering the switch between active and inactive states and decreasing cellular levels of cAMP. Interestingly, Asp294His showed increased ligand affinity but decreased protein activity, highlighting that tighter binding does not always lead to increased activation. Our study provides insights into the molecular mechanisms underlying the impact of MC1R mutations on protein activity.
... The output of these MD simulations are often long multivariate time series describing the position of every atom over time, which are generally challenging to analyze. Traditional statistical techniques [31][32][33][34] , network theory 35,36 , and artificial intelligence 37 are among the most implemented and promising quantitative tools to help unravel complex patterns in these large multidimensional datasets. ...
The ability Gram-negative pathogens have at adapting and protecting themselves against antibiotics has increasingly become a public health threat. Data-driven models identifying molecular properties that correlate with outer membrane (OM) permeation and growth inhibition while avoiding efflux could guide the discovery of novel classes of antibiotics. Here we evaluate 174 molecular descriptors in 1260 antimicrobial compounds and study their correlations with antibacterial activity in Gram-negative Pseudomonas aeruginosa. The descriptors are derived from traditional approaches quantifying the compounds’ intrinsic physicochemical properties, together with, bacterium-specific from ensemble docking of compounds targeting specific MexB binding pockets, and all-atom molecular dynamics simulations in different subregions of the OM model. Using these descriptors and the measured inhibitory concentrations, we design a statistical protocol to identify predictors of OM permeation/inhibition. We find consistent rules across most of our data highlighting the role of the interaction between the compounds and the OM. An implementation of the rules uncovered in our study is shown, and it demonstrates the accuracy of our approach in a set of previously unseen compounds. Our analysis sheds new light on the key properties drug candidates need to effectively permeate/inhibit P. aeruginosa, and opens the gate to similar data-driven studies in other Gram-negative pathogens.
... Throughout the simulation period, the distances of different hydrogen bonds formed were also monitored and analysed. Finally, different non-bond interactions were also analysed from the average interaction of the protein-ligand complexes and compared with the interactions obtained from the starting structures [67][68][69] . ...
To find out effective new antibacterial agents, a series of novel aryl-hydrazothiazolyl-sulfonamide derivatives 3a-e were synthesized and well characterized by analytical and spectroscopic techniques. All the compounds were evaluated for their antibacterial and antifungal potential and the results showed excellent antimicrobial activity of 3d against all strains, especially B. cereus (MIC = 5.54 μM vs. 17.6 μM), P. aeruginosa (MIC = 7.3 μM vs.12.8 μM), E. coli (MIC = 6.4 μM vs. 21.3 μM) and C. albicans (MIC = 6.8 μM vs. 26.4 μM) in comparison to the commercial antibiotics, tetracycline and amphotericin B, respectively. The others analogues dispalyed potent to moderate antimicrobial activity. Structure-activity relationships (SARs) provides specially that incorporation of pyrimidin-2-ylsulfamoyl group at the para-phenyl position (3d) is good contributor for improving he antimicrobial activity. Further, Density Functional Theory (DFT) on 3d revealed its high stability and strongest electron-donating capability. Molecular docking and dynamic simulation studies on 3d inside the active site of S. aureus tyrosyl-tRNA synthetase (PDB ID: 1JIJ), E. coli dihydropteroate synthetase (PDB ID: 1AJ0), C. albicans dihydropteroate synthetase (PDB ID: 4HOE), and C. albicans N-myristoyl transferase (PDB ID: 1IYL) showed good binding profiles with the targets proteins, in particular with 1IYL and 4HOE receptors via the involvement of hydrogen bonding and that the formed complexes were thermodynamically highly stable. Taken together, our results make 3d as a promising lead for further drugs development.
... This may be due mainly to the low sampling of data resulting from molecular dynamics, which tends to make statistically significant even very small variations. Despite of all, a statistical approach is found in the literature that used ANOVA to study the impacts on the activation of the GPCR protein (Bruzzese, Dalton, & Giraldo, 2020). It should be noted that the tests of statistical significance only make sense when running simulations at a time adequate to the studied problem. ...
The novel coronavirus has been causing sad losses around the world and the emergence of new variants has caused great concern. This pandemic is of a proportion not seen since the Spanish Flu in 1918. Thus, throughout this research, the B.1.1.28 lineage of the P.1 clade (K417T, N501Y, E484K) that emerged in Brazil was studied, as well as the latest Delta variant. This is because the molecular mechanisms by which phenotypic changes in transmissibility or mortality remain unknown. Through molecular dynamics simulations with the NAMD 3 algorithm in the interval, it was possible to understand the impact on structural stabilization on the interaction of the ACE2-RBD complex, as well as simulations in for the neutralizing antibody P2B-2F6, with this antibody was derived from immune cells from patients infected with SARS-CoV-2. Although not all molecular dynamics analyzes support the hypothesis of greater stability in the face of mutations, there was a predominance of low fluctuations. Thus, 3 (three) analyzes corroborate the hypothesis of greater ACE2-RBD stability as a result of P.1, among them: Low mean RMSF values, greater formation of hydrogen bonds and low solvent exposure measured by the SASA value. An inverse behavior occurs in the interaction with neutralizing antibodies, since the mutations induce greater instability and thus hinder the recognition of antibodies in neutralizing the Spike protein, where we noticed a smaller number of hydrogen bonds as a result of P.1. Through MM-PBSA energy decomposition, we found that Van der Waals interactions predominated and were more favorable when the structure has P.1 strain mutations. Therefore, we believe that greater stabilization of the ACE2-RBD complex may be a plausible explanation for why some mutations are converging in different strains, such as E484K and N501Y. The P.1 concern variant still makes the Spike protein recognizable by antibodies, and therefore, even if the vaccines' efficacy can be diminished, there are no results in the literature that nullify them.
... The question arises which is the proper reference state for the system. Both the apo receptor 87 and an antagonist-bound receptor may, in principle, be suitable for this purpose. We chose the latter condition because of the high stability of an antagonist-bound inactive receptor state, as well as the potential to deactivate an active receptor state to the inactive. ...
Over the past two decades, the opioid epidemic in the United States and Canada has evidenced the need for a better understanding of the molecular mechanisms of medications used to fight pain. Morphine and fentanyl are widely used in opiate-mediated analgesia for the treatment of chronic pain. These compounds target the μ-opioid receptor (MOR), a class A G protein-coupled receptor (GPCR). In light of described higher efficacy of fentanyl with respect to morphine, we have performed independent μs-length unbiased molecular dynamics (MD) simulations of MOR complexes with each of these ligands, including the MOR antagonist naltrexone as a negative control. Consequently, MD simulations totaling 58 μs have been conducted to elucidate at the atomic level ligand-specific receptor activity and signal transmission in the MOR. In particular, we have identified stable binding poses of morphine and fentanyl, which interact differently with the MOR. Different ligand-receptor interaction landscapes directly induce sidechain conformational changes of orthosteric pocket residues: Asp1493.32, Tyr1503.33, Gln1262.60, and Lys2355.39. The induced conformations determine Asp1493.32-Tyr3287.43 sidechain-sidechain interactions and Trp2956.48-Ala2425.46 sidechain-backbone H-bond formations, as well as Met1533.36 conformational changes. In addition to differences in ligand binding, different intracellular receptor conformational changes are observed as morphine preferentially activates transmembrane (TM) helices: TM3 and TM5, while fentanyl preferentially activates TM6 and TM7. As conformational changes in TM6 and TM7 are widely described as being the most crucial aspect in GPCR activation, this may contribute to the greater efficacy of fentanyl over morphine. These computationally observed functional differences between fentanyl and morphine may provide new avenues for the design of safer but not weaker opioid drugs because it is desirable to increase the safety of medicines without sacrificing their efficacy.
Cardiovascular disorders are still challenging and are among the deadly diseases. As a major risk factor for atherosclerotic cardiovascular disease, dyslipidemia, and high low‐density lipoprotein cholesterol in particular, can be prevented primary and secondary by lipid‐lowering medications. Therefore, insights are still needed into designing new drugs with minimal side effects. Proprotein convertase subtilisin/kexin 9 (PCSK9) enzyme catalyses protein‐protein interactions with low‐density lipoprotein, making it a critical target for designing promising inhibitors compared to statins. Therefore, we screened for potential compounds using a redesigned PCSK9 conformational behaviour to search for a significantly extensive chemical library and investigated the inhibitory mechanisms of the final compounds using integrated computational methods, from ligand essential functional group screening to all‐atoms MD simulations and MMGBSA‐based binding free energy. The inhibitory mechanisms of the screened compounds compared with the standard inhibitor. K31 and K34 molecules showed stronger interactions for PCSK9, having binding energy (kcal/mol) of −33.39 and −63.51, respectively, against −27.97 of control. The final molecules showed suitable drug‐likeness, non‐mutagenesis, permeability, and high solubility values. The C‐α atoms root mean square deviation and root mean square fluctuation of the bound‐PCSK9 complexes showed stable and lower fluctuations compared to apo PCSK9. The findings present a model that unravels the mechanism by which the final molecules proposedly inhibit the PCSK9 function and could further improve the design of novel drugs against cardiovascular diseases.
Despite recent advances in computational protein science, the dynamic behavior of proteins, which directly governs their biological activity, cannot be gleaned from sequence information alone. To overcome this challenge, we propose a framework that integrates the peptide sequence, protein structure, and protein dynamics descriptors into machine learning algorithms to enhance their predictive capabilities and achieve improved prediction of the protein variant function. The resulting machine learning pipeline integrates traditional sequence and structure information with molecular dynamics simulation data to predict the effects of multiple point mutations on the fold improvement of the activity of bovine enterokinase variants. This study highlights how the combination of structural and dynamic data can provide predictive insights into protein functionality and address protein engineering challenges in industrial contexts.
